Abstract: A document creation support apparatus comprising at least one processor, wherein the processor is configured to: acquire an image and a character string related to the image; extract at least one feature region included in the image; specify a specific region that is a region corresponding to a phrase included in the character string, in the feature region; and present information for supporting creation of a document including the character string based on a result of the specifying.

Abstract: An image analysis apparatus, an image analysis method, and a program for specifying a region of interest from an image in which an arrow is assigned to the region of interest are provided. The above problem is solved by an image analysis apparatus including at least one processor, and at least one memory in which an instruction to be executed by the at least one processor is stored, in which the at least one processor is configured to receive an image in which an arrow is assigned to a region of interest, specify the arrow, dispose one or more region-of-interest candidates that are candidates of the region of interest in accordance with a direction of the arrow and with a distance from the arrow, calculate an interest degree for each region-of-interest candidate, and specify the region of interest from among the region-of-interest candidates based on the interest degree.

Abstract: Provided are a linear structure extraction device, a method, a program, and a learned model which can detect a linear structure in an image. A linear structure extraction device according to an embodiment of the present disclosure includes a learning model that is learned to receive an input of the image and output, as a prediction result, one or more element points which constitute the linear structure from the image, in which the learning model includes a first processing module that receives the image and generates a feature map representing a feature amount of the image by convolution processing, and a second processing module that calculates a shift amount from a unit center point to the element point of the linear structure closest to the unit center point, for each unit obtained by dividing the feature map into a plurality of the units including regions having a predetermined size in a grid pattern.

Abstract: A method of generating a language feature extraction model that causes a computer to extract a feature from a text related to an image, includes that a system performs machine learning using training data including a first image, first position information related to a region of interest in the first image, and a first text that describes the region of interest to input the first text into a first model, which is the language feature extraction model, to cause the first model to output a first feature amount, input the first image and the first feature amount into a second model to cause the second model to estimate the region of interest, and train the first model and the second model such that an estimated region of interest output from the second model matches the region of interest of a correct answer indicated by the first position information.

Abstract: An information processing apparatus, an information processing method, and a program that can determine a region-of-interest candidate of an image using information related to the image are provided. An information processing apparatus includes one or more processors, and one or more storage devices in which an instruction executed by the one or more processors is stored, in which the one or more processors are configured to acquire an image, related information related to the image, and one or more first region-of-interest candidates included in the image, estimate one or more image regions indicated by the related information from the image and from the related information, and determine a second region-of-interest candidate from among the first region-of-interest candidates based on the estimated image region.

Abstract: An information processing apparatus comprising at least one processor, wherein the processor is configured to: acquire a medical image including a plurality of lesion regions; classify a high or low degree of interest of at least one first lesion region among the plurality of lesion regions; specify at least one second lesion region similar to the first lesion region among the plurality of lesion regions in a case where the degree of interest is low; and make a display method of the first lesion region and the second lesion region classified as having a low degree of interest different from a display method of other lesion regions in a case where the plurality of lesion regions are highlighted and the medical image is displayed on a display.

Abstract: An information processing apparatus comprising at least one processor, wherein the processor is configured to: acquire an image; display, on a display, a figure indicating a first region of interest included in the image in a superimposed manner on the image; receive a correction instruction for at least a part of the figure; and specify a second region of interest that at least partially overlaps with the first region of interest based on an image feature of the image and the correction instruction.

Abstract: An information processing apparatus comprising at least one processor, wherein the processor is configured to: acquire a medical image including a plurality of lesion regions; specify a plurality of pieces of first findings information for each of the plurality of lesion regions; and specify second findings information for at least one lesion region among the plurality of lesion regions based on a combination of the plurality of pieces of first findings information.

Abstract: A learning unit uses a learning input image and local annotation data generated by locally giving labels to regions of classes in the learning input image as training data for a region extraction model. The learning unit directs the region extraction model to output a final feature amount map having element values related to probabilities of being the regions of the classes. Then, a sharpening process is performed on a probability distribution map that has been generated on the basis of the final feature amount map and that shows the probability for each class to obtain a processed probability distribution map. The learning unit calculates an average value of pixel values of the boundary image generated based on the processed probability distribution map as a boundary length loss, and updates the region extraction model in a direction in which the average value is reduced.

Abstract: A processor derives a feature amount related to an input image based on first to nth (n?2) derivation models, derives, via the first derivation model, a first feature amount based on the input image, and derives, via a kth (1<k?n) derivation model, a kth feature amount based on the input image and a k?1th feature amount derived by a k?1th derivation model. An nth feature amount derived by the nth derivation model is the feature amount related to the input image, and the k?1th feature amount includes a feature amount related to another region that is independent of a region related to the kth feature amount, which suggests the kth feature amount derived by the kth derivation model, and that serves as a basis for the derivation of the kth feature amount.

Abstract: A medical image analysis apparatus extracts a region having one or more preset physical features from a medical image, performs control to display information indicating the extracted region, receives information indicating a selected region from among the extracted regions, refers to data in which physical features are associated with disease names, and generates a comment on findings using the disease name associated with the physical features of the selected region.

Abstract: A medical image apparatus acquires a medical image, information indicating a plurality of regions of interest included in the medical image, and an attribute of each of the plurality of regions of interest, selects at least one region of interest from among the plurality of regions of interest, and performs control to display information regarding a region of interest other than the selected region of interest based on an attribute of the selected region of interest.

Abstract: Provided are a medical image processing apparatus, method, program, and a diagnosis support apparatus, method, and program that can efficiently collect medical images that have a large contribution to improving the accuracy of a diagnosis using medical images. The medical image processing apparatus includes a reception unit that receives an input of a medical image and patient information corresponding to the medical image, an analysis result acquisition unit that acquires an analysis result obtained by analyzing the medical image, a detection unit that detects whether or not the analysis result has been corrected, and a data processing unit that creates and stores data in which identification information capable of identifying a patient is concealed in a case where it is detected that the analysis result has been corrected.

Abstract: A processor superimposes and displays relevant information related to each of a plurality of regions of interest included in a medical image on the medical image to be switchable between display and hiding, specifies at least one region of attention based on specification information including other information other than information dedicated to specifying regions of attention that a user pays attention to among the plurality of regions of interest, and switches to display relevant information related to the specified region of attention.

Abstract: A processor superimposes and displays relevant information related to a medical image on the medical image so as to be switchable between display and hiding. The processor switches at least a part of the relevant information to be hidden in a case where another operation related to display or interpretation of the medical image is detected, the other operation being an operation other than a dedicated operation for switching between display and hiding of the relevant information while the relevant information is being displayed.

Abstract: A virtual conference in relation to diagnostic object data is created. Messages posted to the virtual conference from terminal devices of users participating in the virtual conference are received, and the posted messages are sent to the other terminal devices beside the terminal devices of the users. Information relating to the diagnostic object data is generated, which information is obtained in accordance with analysis results of analyzing the posted messages. The information relating to the diagnostic object data is sent to the terminal devices.

Abstract: A medical image processing apparatus includes at least one processor, in which the processor is configured to: acquire a result of detecting at least one region of interest included in a medical image, which is detected by analyzing the medical image; specify a region of attention that a user has paid attention to in the medical image; specify a non-attention region of interest, which is a region of interest having a structure different from a structure related to the region of attention, among the regions of interest; and display a result of specifying the non-attention region of interest on a display.

Abstract: Provided are a learning system, a method, and a program that can provide medical institutions with motivation for creating learning data related to medical images in a case of using machine learning to support diagnosis using medical images. The learning system includes a reception unit that receives an input of first learning data from a user, a calculation unit that calculates, a contribution degree of the first learning data to learning of a discriminator for each user on the basis of at least one of a comparison result between the first learning data and second learning data used for creating the discriminator or a comparison result between an output obtained by inputting the first learning data to the discriminator and correct answer data corresponding to the first learning data, and a service setting unit that sets a service for the user on the basis of the contribution degree calculated for each user.

Abstract: The processor divides a sentence into predetermined units, determines at least one of an attribute, a factuality, or a relationship of each unit, decides a weight for each unit according to a result of the determination, and derives a feature amount of each unit by using a derivation model constructed by machine learning and derive a feature amount of the sentence by performing weighting calculation on the feature amount of each unit based on the weight.

Abstract: A processor derives a first feature amount for each of a plurality of first objects included in first data by a first neural network, derives a second feature amount for second data including one or more second objects by a second neural network, specifies a first object candidate, which is paired with the second object, from among the plurality of first objects, estimates an attribute of a pair second object, which is paired with the first object candidate, based on the first object candidate, and trains at least one of the first neural network or the second neural network such that a difference between the estimated attribute of the pair second object and an attribute of the pair second object derived from the second data is reduced.